Photos, Plate maps and strain-id to orf-name table
Photo ethics
Things to keep in mind while taking pictures of the plates:
Camera should be at maximum zoom
Make sure the camera is properly leveled
When in doubt use the level tool in the lab by placing it parallel to the length and width of the camera LCD
The black background should cover the entire field of view
Plate orientation - the color strips (specifying media) should point away from you
Photos should be organized as Experiment > Arm > Stage > Hours
Within the Hours folder the photos should be arranged in the same order as the plate names/number
Example
If you were conducting an experiment using the mutant collection
The experiment had two parallel arms going from 384 density plates (Starter Plates) to 1536 density plates (Pre-screen) to 6144 density plates (Final Screen)
Photos for the Starter Plates and Pre-screen were taken at saturation and those for Final Screen were taken at 0, 4 and 12 hours
Then the folder heirarchy would be as follows:
Experiment
Arm #1
Starter Plates
36h
Plate 1
Plate 2
Pre-screen
20h
Plate A
Plate B
Final Screen
00h
04h
12h
Arm #2
Starter Plates
36h
Pre-screen
20h
Final Screen
00h
04h
12h
If the bifurcation of the arms occur later in the experiment then the folder heirarchy could be as follows:
Experiment
Starter Plates
36h
Plate 1
Plate 2
Pre-screen
20h
Plate A
Plate B
Arm #1
Final Screen
00h
04h
12h
Arm #2
Final Screen
00h
04h
12h
Make sure the terminal folders containing the photos are names is a 'tth' manner like shown above
If the photos weren't organized while taking them then use an STFP client to download all photos to your local computer, organize them and upload.
Use an SFTP client like FileZilla for Mac and Windows or WinSCP for Windows.
Use SFTP clients mentioned earlier to upload all files to the MATLAB folder.
If this is your first time then perform STEP 2 before uploading the files.
STEP 2: Download LI Detector
Download LID scripts and the necessary dependencies
Points 1 to 5 only needs to be performed during your first time
Otherwise proceed to point 6
Connect to paris from terminal
Both ssh -X and ssh -Y are ok to use. ssh -Y works more consistently for me.
ssh -Y username@paris.csb.pitt.edu
Refer to Computational Resources on Paris section of the Lab Manual to make sure you have all the necessary components to run MATLAB GUI on paris server.
Install Database Toolbox, Image Processing Toolbox and Parallel Computing Toolbox from the APPS > Get More Apps option within MATLAB
Copy dependencies to your MATLAB folder.
cd MATLAB
cp /home/sbp29/MATLAB/mysql-connector-java-8.0.16.jar ./
Copy the download lid bash script to your MATLAB folder and make it executable.
Specify sub-directory to store all the scripts when prompted
The sub-directory will be made if it doesn't exist
Pressing enter will save everything to the MATLAB folder itself
./download_lid.sh
Navigate to the lidetector folder for executing the next steps.
cd<sub-directory name from point 5>/lidetector
Video walkthrough:
STEP 3: Initialize
Generating tables necessary for next steps
Information to keep in handy before proceeding:
Path to your MALTAB directory
most likely to be /home/username/MATLAB
Path to tbe sub-directory from Step 2, point 5
If everything is stored within the MATLAB folder itself, then pressing enter when prompted will suffice.
MySQL credentials - username, password, database name
Upscale patterns from the experiment - ie in what combinations were the lower density plates condensed to form the higher density plates
Name of reference strain being used
Should one of the orf-name in the files from step 1
Path to files created in Step 1
Successful run will create the following tables:
_borderpos = border positions of all plates in the experiment
1 border for 384 density, 2 for 1536 and 4 for 6144
_pos2coor = position ids and their corresponding plate coordinate
unique position ids for all possible colony positions in the experiment and thei correspoing plate coordinates ie colony density, plate number, row number and column number
_pos2orf_name = position ids and the corresponding orf-name (or mutant name)
_pos2rep = position ids of lowest density plates to their replicates at higher density plates based on the upscale pattern
for internal use
_pos2strain_id = position ids and their corresponding strain ids
_strainid2orf_name = table from point 3 of step 1
Execute
./initialize.sh
Video walkthrough:
STEP 4: Analyze Images
Pixel count estimation
Information to keep in handy before proceeding:
Path to your MALTAB directory
most likely to be /home/username/MATLAB
Path to tbe sub-directory from Step 2, point 5
If everything is stored within the MATLAB folder itself, then pressing enter when prompted will suffice.
Path to the 'Stage' level photos from Step 1
Location of any smudges on the plates ie the colonies you want to remove from the analysis because of any technical issues
plate number, row number, column number
User will be asked to verify binary files before uploading raw pixel count data
Use SFTP clients mentioned above to download the photo folder from Step 1
Each image will now have 3 additional files - .binary, .cs.txt and .info.mat
View the .binary file (using Preview in Mac) to verify if the colonies have been correctly identified
Original image:
Good binary image:
Bad binary image:
Successful run will create the following tables:
_RAW = raw colony size estimations per hour per position id of all the images
image1, image2 and image3 columns correspond to the three images per plate
average column is the mean of the pixel count estimation from the three images
image1 = image2 = image3 = average if there is a single image per plate
_smudgebox = position ids corresponding to the user defined coordinates
_JPEG = similar to _RAW with
pixel count estimations for borders and smudgebox NULL'd
and any pixel count estimation < 10 is also NULL'd - likely to be a light artifact
Execute
./imageanalyzer.sh
Video walkthrough:
STEP 5: Spatial Bias Correction
Relative fitness measurements and p-value estimation
Information to keep in handy before proceeding:
Path to your MALTAB directory
most likely to be /home/username/MATLAB
Path to tbe sub-directory from Step 2, point 5
If everything is stored within the MATLAB folder itself, then pressing enter when prompted will suffice.
Successful run will create the following tables:
_NORM = position ids and their corresponding relative fitness measurements
also includes the background pixel count measurement based on references
_FITNESS = similar to _NORM but with strain ids and orf-names included
_FITNESS_STAT = strain-id-wise mean, median and standard deviation of relative fitness
_PVALUE = strain-id-wise empirical p-values
stat = (strain mean fitness - reference mean fitness)/reference fitness standard deviation
es = (strain mean fitness - reference mean fitness)reference mean fitness